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From |
Vitor Felipe <vfgate-demog@yahoo.com.br> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Tobit coefficients |

Date |
Fri, 27 Jul 2007 13:02:06 -0300 (ART) |

Thank you very much Austin!! This is exactly what I was looking for!! Perfect explanation. Sincerely, Vitor --- Austin Nichols wrote: > Vitor <vfgate-demog@yahoo.com.br>: > As I have pointed out before on this list, e.g. > http://www.stata.com/statalist/archive/2007-04/msg00549.html > http://www.stata.com/statalist/archive/2006-12/msg00466.html > -tobit- is inappropriate if the zeros are not > censored values > (representing a negative y* that is observed as > y=0). If the zeros > are simply a point mass in the distribution of a > nonnegative dep var, > then -poisson- or -glm- are better options. In your > case, the fact > that many people receive no money from parents does > not mean that > their parents would like to take money from them > (make a negative > transfer), but are prevented by law from doing so. > I would use > -poisson- to estimate the response of ln(y) > including zeros of y in > the estimation, and then predict as usual. Why do > you need a > prediction conditional on y>0 anyway? Just to > ensure predictions are > nonnegative? Bad reason! > > Though -poisson- is designed for count variables, it > works well for > any model where E(y|x)=exp(xb). See Wooldridge > (http://www.stata.com/bookstore/cspd.html) p.651 and > surrounding text: > "A nice property of the Poisson QMLE is that it > retains some > efficiency for certain departures from the Poisson > assumption." > > In any case, the better way to get the predictions > is to predict over > the whole estimation sample, but first replace 1) > AFTER=1, TREAT=1, > TREAT*AFTER=1, then 2) AFTER=1, TREAT=0, > TREAT*AFTER=0, etc. to > generate predictions *for each observation* under > different > counterfactuals, then compare the means (the use of > -poisson- is > illustrative--any estimation command can be used, > even -tobit-): > > sysuse auto, clear > replace mpg=max(0,mpg-20) > g treat=rep78>3 if !mi(rep78) > la var treat "Fake Treatment Var" > ren for after > la var after "Fake After Var" > g ta=treat*after > poisson mpg len treat after ta > predict m if e(sample) > replace treat =1 > replace after=1 > replace ta=treat*after > predict m11 if e(sample) > replace treat =0 > replace ta=treat*after > predict m01 if e(sample) > replace after=0 > replace ta=treat*after > predict m00 if e(sample) > replace treat =1 > replace ta=treat*after > predict m10 if e(sample) > su m01 > local m01=r(mean) > su m11 > local m11=r(mean) > di "Effect of treat given after=1 is " `m11'-`m01' > su m00 > local m00=r(mean) > su m10 > local m10=r(mean) > di "DD of treat, after switching 0->1, is " > `m11'-`m01'-`m10'+`m00' > > and so on. Then you could wrap all that in a rclass > -program- and > -bootstrap- it for standard errors on the estimated > treatment effects, > not that the diff-in-diff will really identify any > true treatment > effects. Alertas do Yahoo! Mail em seu celular. Saiba mais em http://br.mobile.yahoo.com/mailalertas/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: RE: Tobit coefficients***From:*"Austin Nichols" <austinnichols@gmail.com>

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